Name
Abstract | ||
|---|---|---|
Hit-and-Run is a well-known class of Markov chain algorithms for sampling from essentially arbitrary distributions over bounded regions of the Euclidean space. We present a class of Small World network models constructed using Hit-and-Run in a Euclidean ball. We prove that there is a unique scale invariant model in this class that admits efficient search by a decentralized algorithm. This research links two seemingly unrelated areas: Markov chain sampling techniques and scale invariant Small World networks, and may have interesting implications for stochastic search methods for continuous optimization. © 2008 Wiley Periodicals, Inc. NETWORKS, 2009 |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1002/net.v53:1 | Networks |
Keywords | Field | DocType |
sampling technique,markov chain,continuous optimization,scale invariance,small world networks,euclidean space,small world network | Continuous optimization,Combinatorics,Search algorithm,Small-world network,Markov chain,Euclidean space,Invariant (mathematics),Stochastic programming,Mathematics,Bounded function | Journal |
Volume | Issue | ISSN |
53 | 1 | 0028-3045 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Archis Ghate | 1 | 124 | 17.82 |
| Robert L. Smith | 2 | 664 | 123.86 |